The Comparison of the Shadow Economy in Turkey and European Countries

2016 ◽  
pp. 73-105 ◽  
Author(s):  
Coskun Karaca
Keyword(s):  
Informal Economy ◽  
Shadow Economy ◽  
Reliable Data ◽  
European Countries ◽  
Measuring Methods ◽  
Currency Demand ◽  
Policy Proposals

As informal activities are considered as a crime, that kind of activities are being carried out secretly and their detection is difficult in most cases. Along with difficulties in determining the size of informal economy exactly, recently developed models and opportunities to reach reliable data enable making realistic estimations in regard to shadow economy. This study benefits from 11 different studies estimating informality in European countries and Turkey by using physical input, currency demand, DYMIMIC, and MIMIC methods. Common conclusion acquired from these studies is that informality rate in Turkey is higher than EU15 countries and EU13 countries –except for Hungary, Cyprus, Latvia, Croatia and Bulgaria. In addition to the comparison of these data, the reasons of the emergence of informal economy, measuring methods, and policy proposals in order to hamper informality in Turkey are also discussed.

Intensity of Regulations as a Cause of the Informal Sector

2020 ◽  
Vol 15 (2) ◽  
pp. 135-154
Author(s):  
Khurrum S. Mughal ◽  
Friedrich G. Schneider ◽  
Zafar Hayat
Keyword(s):  
Informal Sector ◽  
Informal Economy ◽  
Shadow Economy ◽  
Control Variable ◽  
Government Control ◽  
Yearly Variation ◽  
New Variant ◽  
History Of ◽  
Currency Demand ◽  
And Control

It is argued in the literature that the intensity of regulations and control in an economy is a determinant of the informal sector which however is ignored in most of its estimates. This article uses a new variant of the currency demand approach where ‘unemployment’ and ‘intensity of government control’ are used to estimate a shadow economy, alongside a the traditional tax variable. We choose Pakistan since it has a significant share of its activities in the informal sector along with the history of various political and dictatorial regimes. Further, there are examples of bureaucratic control leading to corruption in the economy. It provides an opportunity to study the nexus between regulation intensity and informal economy and present a case study for other developing countries exercising control over the economy through the large size of its public sector. The results show that the intensity of the control variable has statistically and economically significant role in increasing the shadow economy, almost equivalent to the tax coefficient. Once the yearly variation in our estimates is mapped with various political regimes, it seems that the validity of estimates is reinforced considering policy inconsistencies and prominent events of each regime.

scholarly journals Currency demand and MIMIC models: towards a structured hybrid method of measuring the shadow economy

2018 ◽  
Vol 26 (1) ◽  
pp. 4-40 ◽  
Author(s):  
Piotr Dybka ◽  
Michał Kowalczuk ◽  
Bartosz Olesiński ◽  
Andrzej Torój ◽  
Marek Rozkrut
Keyword(s):  
Hybrid Method ◽  
Shadow Economy ◽  
Mimic Models ◽  
Currency Demand

scholarly journals SOME NEW ESTIMATES OF SHADOW ECONOMY FOR 80 COUNTRIES USING POOLED MEAN GROUP ESTIMATOR

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Yan-Ling Tan ◽  
Muzafar Shah Habibullah ◽  
Shivee Ranjanee Kaliappan ◽  
Alias Radam
Keyword(s):  
Shadow Economy ◽  
Factor Model ◽  
Statistical Significance ◽  
Dynamic Factor ◽  
Pooled Mean Group ◽  
Macroeconomic Uncertainty ◽  
Long Run ◽  
Uncertainty Index ◽  
Short Run ◽  
Currency Demand

The purpose of this study is to estimates the size of the shadow economy for 80 countries from nine regions spanning the period 1975-2012 based on Tanzi-type currency demand approach (CDA). This study contributes to the literature in three distinct ways. First, we augment CDA regression with a macroeconomic uncertainty index (MUI). Second, the construction of the uncertainty index is based on the dynamic factor model (DFM). Third, the pooled mean group (PMG) estimator allows in capturing the heterogeneity across countries in the short-run dynamics but imposing restrictions in the long-run parameters. The results confirm the existence of the longrun equilibrium relationship among the variables examined. All coefficients show expected signs along with statistical significance. More importantly, the macroeconomic uncertainty index variable show positive relationship, suggesting that public tend to hold more currency in an uncertain macroeconomic environment. In addition, we observe that developing regions (ranging from 19.9% to 37.3%) exhibit relatively large size of the shadow economy. On the contrary, developed regions have a considerable smaller estimate (ranging from 13.7% to 19.0%) of the size of shadow economy. On average, the world estimate of the shadow economy as a percentage of GDP is about 23.1%. Keywords: Shadow Economy; Currency Demand; Macroeconomic Uncertainty; Pooled Mean Group.

scholarly journals Raising the Accuracy of Shadow Economy Measurements

2021 ◽  
Vol 239 (4) ◽  
pp. 71-125
Author(s):  
Vicente Ríos ◽  
◽  
Antonio Gómez ◽  
Pedro Pascual ◽  
◽  
...  
Keyword(s):  
Bayesian Model ◽  
Latent Variable ◽  
Bayesian Model Averaging ◽  
Shadow Economy ◽  
Electricity Consumption ◽  
Model Averaging ◽  
Hidden Sector ◽  
Multiple Indicators ◽  
Econometric Methods ◽  
Currency Demand

This article estimates the size of the shadow economy in a Spanish region (Navarre) for the period 1986- 2016. To this end, we employ indirect macro-econometric methods such as the Currency Demand approach, Electricity Consumption (Physical Input) methods and the multiple indicators multiple causes (MIMIC) approach. A differential feature of our empirical analysis is that we incorporate various methodological innovations (e..g. Bayesian Model Averaging, a Time-Varying Parameter model, normalization of the latent variable) to refine and increase the measurement accuracy of each of the indirect methods considered. The temporal pattern of the shadow economy’s size that emerges from the different approaches is similar, which suggests that the estimates obtained are robust and capture the underlying dynamics of the hidden sector. After quantifying the shadow economy, we analyze its determinants by means of Bayesian Model Averaging techniques. We find that the evolution of the shadow economy in Navarre can be explained by a small and robust set of factors, specifically the tax burden, the share of employment in the construction sector, the inflation rate, euro area membership and the ratio of currency outside the banks to M1.

scholarly journals Industrial China and its impact on Informality.

2017 ◽  
Vol 8 (2) ◽  
pp. 895-906
Author(s):  
Kevin Michael Fleary
Keyword(s):  
Conceptual Model ◽  
Informal Economy ◽  
Turning Point ◽  
National Development ◽  
Policy Decision ◽  
Decision Makers ◽  
Central Theme ◽  
Demand Model ◽  
Economic Changes ◽  
Currency Demand

The informal economy has been the topic of debate for some time now and given its significant effect on the revenue, economists and policy decision makers alike have sought greater control to their dismay. This can be contributed to the lack of informal statistics on China, fueling assumptions that have not born results. To address the problem, this paper seeks to estimate the size of China’s informal economy using the Currency Demand Model. The central theme forwarded in this research is that China is experiencing its Lewis Turning Point and the industry changes positively impact the growth of the underground economy. Given this statement, the authors present a conceptual model that depicts the growth and movement of the informal economy in relation to China’s economic changes. As a means to provide intuition for decision makers as China transition to the 13th National Development Plan.

scholarly journals Lifting of International Sanctions and the Shadow Economy in Iran—A View from Outer Space

2021 ◽  
Vol 13 (22) ◽  
pp. 4620
Author(s):  
Mohammad Reza Farzanegan ◽  
Sven Fischer
Keyword(s):  
Gross Domestic Product ◽  
Informal Economy ◽  
Shadow Economy ◽  
Outer Space ◽  
Annual Increase ◽  
Comprehensive Plan ◽  
Formal Economy ◽  
Nighttime Lights ◽  
Short Period ◽  
International Sanctions

With the implementation of the Joint Comprehensive Plan of Action (JCPOA) in 2016, Iran experienced a short period without international sanctions which resulted in an annual increase in the gross domestic product (GDP) in the following two years. However, it was not just the formal economy that was affected by the sanctions. Previous studies have shown that sanctions can negatively affect the shadow (or informal) economy and may even have a larger impact on the informal economy than on the formal economy. Nighttime lights (NTL) data allow us to study shadow economy activities that are not reported in the official GDP. This study uses a panel of NTL (the DMSP/OLS and VIIRS/DNB harmonized dataset) from 1992 to 2018 for 31 Iranian provinces to investigate the association between the lifting of sanctions and the growth of the shadow economy. The empirical results suggest an increase in shadow economy activity with the lifting of sanctions while controlling for other drivers of informal activities.

scholarly journals The Relationship between Corruption and the Shadow Economy in Ukraine and Other Central and Eastern European Countries

2020 ◽  
Vol 23 (4) ◽  
pp. 7-30
Author(s):  
Valentyna Cherviakova ◽  
Tetiana Cherviakova
Keyword(s):  
Regression Analysis ◽  
Shadow Economy ◽  
Statistical Data ◽  
Statistical Significance ◽  
European Countries ◽  
Eastern European ◽  
Weak Correlation ◽  
Low Levels ◽  
Eastern European Countries ◽  
The Relationship

The article investigates the causes, essence, and peculiarities of corruption and the shadow economy, as well as how they are related, in Ukraine in comparison with other Central and Eastern European countries. A correlation‑regression analysis of statistical data revealed a direct correlation connection of different strengths and statistical significance between levels of corruption and the shadow economy in all Central and Eastern European countries. However, the degree to which corruption impacts the variation in the levels of the shadow economy differs significantly in countries across the region. The key conclusion is that in countries with relatively high levels of corruption and the shadow economy, corruption causes a smaller share of the shadow economy than in countries with relatively low levels of these phenomena. Causes of the weak correlation between levels of corruption and the shadow economy in Ukraine were identified. The main corruption and non‑corruption factors of Ukraine’s economy shadowing were determined. It was concluded that policy and measures to counteract corruption and the shadow economy in Ukraine should be aimed at eliminating their root causes rather than manifestations.

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